Patents by Inventor Marcus Eng Hock Ong
Marcus Eng Hock Ong has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 11647963Abstract: A method of producing an artificial neural network capable of predicting the survivability of a patient, including: storing in an electronic database patient health data comprising a plurality of sets of data, each set having at least one of a first parameter relating to heart rate variability data and a second parameter relating to vital sign data, each set further having a third parameter relating to patient survivability; providing a network of nodes interconnected to form an artificial neural network, the nodes comprising a plurality of artificial neurons, each artificial neuron having at least one input with an associated weight; and training the artificial neural network using the patient health data such that the associated weight of the at least one input of each artificial neuron is adjusted in response to respective first, second and third parameters of different sets of data from the patient health data.Type: GrantFiled: December 8, 2020Date of Patent: May 16, 2023Assignees: SINGAPORE HEALTH SERVICES PTE LTD., NANYANG TECHNOLOGICAL UNIVERSITYInventors: Marcus Eng Hock Ong, Zhiping Lin, Wee Ser, Guangbin Huang
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Publication number: 20210251575Abstract: A method of producing an artificial neural network capable of predicting the survivability of a patient, including: storing in an electronic database patient health data comprising a plurality of sets of data, each set having at least one of a first parameter relating to heart rate variability data and a second parameter relating to vital sign data, each set further having a third parameter relating to patient survivability; providing a network of nodes interconnected to form an artificial neural network, the nodes comprising a plurality of artificial neurons, each artificial neuron having at least one input with an associated weight; and training the artificial neural network using the patient health data such that the associated weight of the at least one input of each artificial neuron is adjusted in response to respective first, second and third parameters of different sets of data from the patient health data.Type: ApplicationFiled: December 8, 2020Publication date: August 19, 2021Inventors: Marcus Eng Hock Ong, Zhiping Lin, Wee Ser, Guangbin Huang
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Patent number: 10888282Abstract: A method of producing an artificial neural network capable of predicting the survivability of a patient, including: storing in an electronic database patient health data comprising a plurality of sets of data, each set having at least one of a first parameter relating to heart rate variability data and a second parameter relating to vital sign data, each set further having a third parameter relating to patient survivability; providing a network of nodes interconnected to form an artificial neural network, the nodes comprising a plurality of artificial neurons, each artificial neuron having at least one input with an associated weight; and training the artificial neural network using the patient health data such that the associated weight of the at least one input of each artificial neuron is adjusted in response to respective first, second and third parameters of different sets of data from the patient health data.Type: GrantFiled: October 16, 2018Date of Patent: January 12, 2021Assignees: SINGAPORE HEALTH SERVICES PTE LTD., NANYANG TECHNOLOGICAL UNIVERSITYInventors: Marcus Eng Hock Ong, Zhiping Lin, Wee Ser, Guangbin Huang
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Patent number: 10299689Abstract: The present disclosure provides a system and method of determining a risk score for triage. In particular, a system is provided for providing an assessment of risk of a cardiac event for a patient, for example an incoming patient to a hospital emergency department complaining of chest pain. In the disclosure, the system includes an input device for measuring physiological data based vital signs parameter of the patient, a twelve-lead electrocardiogram (ECG) device for establishing an ECG obtained from results of the electrocardiography procedure, and determining an ECG parameter and a heart rate variability (HRV) parameter therefrom. An ensemble-based scoring system is further provided, establishing weighted classifier based on past patient data and where the vital signs parameter, the ECG parameter and the HRV parameter are compared to corresponding weighted classifiers to determine a risk score. A corresponding method to determine a risk score for triage is also provided.Type: GrantFiled: March 7, 2014Date of Patent: May 28, 2019Assignee: SINGAPORE HEALTH SERVICES PTE LTDInventors: Marcus Eng Hock Ong, Nan Liu
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Publication number: 20190150850Abstract: A method of producing an artificial neural network capable of predicting the survivability of a patient, including: storing in an electronic database patient health data comprising a plurality of sets of data, each set having at least one of a first parameter relating to heart rate variability data and a second parameter relating to vital sign data, each set further having a third parameter relating to patient survivability; providing a network of nodes interconnected to form an artificial neural network, the nodes comprising a plurality of artificial neurons, each artificial neuron having at least one input with an associated weight; and training the artificial neural network using the patient health data such that the associated weight of the at least one input of each artificial neuron is adjusted in response to respective first, second and third parameters of different sets of data from the patient health data.Type: ApplicationFiled: October 16, 2018Publication date: May 23, 2019Inventors: Marcus Eng Hock Ong, Zhiping Lin, Wee Ser, Guangbin Huang
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Patent number: 10136861Abstract: A method of producing an artificial neural network capable of predicting the survivability of a patient, including: storing in an electronic database patient health data comprising a plurality of sets of data, each set having at least one of a first parameter relating to heart rate variability data and a second parameter relating to vital sign data, each set further having a third parameter relating to patient survivability; providing a network of nodes interconnected to form an artificial neural network, the nodes comprising a plurality of artificial neurons, each artificial neuron having at least one input with an associated weight; and training the artificial neural network using the patient health data such that the associated weight of the at least one input of each artificial neuron is adjusted in response to respective first, second and third parameters of different sets of data from the patient health data.Type: GrantFiled: September 20, 2017Date of Patent: November 27, 2018Assignees: SINGAPORE HEALTH SERVICES PTE LTD., NANYANG TEHNOLOGICAL UNIVERSITYInventors: Marcus Eng Hock Ong, Zhiping Lin, Wee Ser, Guangbin Huang
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Publication number: 20180098736Abstract: A method of producing an artificial neural network capable of predicting the survivability of a patient, including: storing in an electronic database patient health data comprising a plurality of sets of data, each set having at least one of a first parameter relating to heart rate variability data and a second parameter relating to vital sign data, each set further having a third parameter relating to patient survivability; providing a network of nodes interconnected to form an artificial neural network, the nodes comprising a plurality of artificial neurons, each artificial neuron having at least one input with an associated weight; and training the artificial neural network using the patient health data such that the associated weight of the at least one input of each artificial neuron is adjusted in response to respective first, second and third parameters of different sets of data from the patient health data.Type: ApplicationFiled: September 20, 2017Publication date: April 12, 2018Inventors: Marcus Eng Hock Ong, Zhiping Lin, Wee Ser, Guangbin Huang
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Patent number: 9795342Abstract: A method of producing an artificial neural network capable of predicting the survivability of a patient, including: storing in an electronic database patient health data comprising a plurality of sets of data, each set having at least one of a first parameter relating to heart rate variability data and a second parameter relating to vital sign data, each set further having a third parameter relating to patient survivability; providing a network of nodes interconnected to form an artificial neural network, the nodes comprising a plurality of artificial neurons, each artificial neuron having at least one input with an associated weight; and training the artificial neural network using the patient health data such that the associated weight of the at least one input of each artificial neuron is adjusted in response to respective first, second and third parameters of different sets of data from the patient health data.Type: GrantFiled: August 3, 2016Date of Patent: October 24, 2017Assignees: SINGAPORE HEALTH SERVICES PTE LTD., NANYANG TECHNOLOGICAL UNIVERSITYInventors: Marcus Eng Hock Ong, Zhiping Lin, Wee Ser, Guangbin Huang
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Patent number: 9775533Abstract: The present disclosure provides a system and method of determining a risk score for triage. In particular, a system is provided for providing an assessment of risk of a cardiac event for a patient, for example an incoming patient to a hospital emergency department complaining of chest pain. In the disclosure, the system includes an input device for measuring physiological data based vital signs parameter of the patient, a twelve-lead electrocardiogram (ECG) device for establishing an ECG obtained from results of the electrocardiography procedure, and determining an ECG parameter and a heart rate variability (HRV) parameter therefrom. An ensemble-based scoring system is further provided, establishing weighted classifier based on past patient data and where the vital signs parameter, the ECG parameter and the HRV parameter are compared to corresponding weighted classifiers to determine a risk score. A corresponding method to determine a risk score for triage is also provided.Type: GrantFiled: March 8, 2013Date of Patent: October 3, 2017Assignee: SINGAPORE HEALTH SERVICES PTE LTDInventors: Marcus Eng Hock Ong, Nan Liu
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Publication number: 20170049403Abstract: A method of producing an artificial neural network capable of predicting the survivability of a patient, including: storing in an electronic database patient health data comprising a plurality of sets of data, each set having at least one of a first parameter relating to heart rate variability data and a second parameter relating to vital sign data, each set further having a third parameter relating to patient survivability; providing a network of nodes interconnected to form an artificial neural network, the nodes comprising a plurality of artificial neurons, each artificial neuron having at least one input with an associated weight; and training the artificial neural network using the patient health data such that the associated weight of the at least one input of each artificial neuron is adjusted in response to respective first, second and third parameters of different sets of data from the patient health data.Type: ApplicationFiled: August 3, 2016Publication date: February 23, 2017Inventors: Marcus Eng Hock Ong, Zhiping Lin, Wee Ser, Guangbin Huang
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Patent number: 9420957Abstract: A method of producing an artificial neural network capable of predicting the survivability of a patient, including: storing in an electronic database patient health data comprising a plurality of sets of data, each set having at least one of a first parameter relating to heart rate variability data and a second parameter relating to vital sign data, each set further having a third parameter relating to patient survivability; providing a network of nodes interconnected to form an artificial neural network, the nodes comprising a plurality of artificial neurons, each artificial neuron having at least one input with an associated weight; and training the artificial neural network using the patient health data such that the associated weight of the at least one input of each artificial neuron is adjusted in response to respective first, second and third parameters of different sets of data from the patient health data.Type: GrantFiled: February 6, 2015Date of Patent: August 23, 2016Assignees: SINGAPORE HEALTH SERVICES PTE LTD., NANYANG TECHNOLOGICAL UNIVERSITYInventors: Marcus Eng Hock Ong, Zhiping Lin, Wee Ser, Guangbin Huang
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Patent number: 9295429Abstract: A method of predicting survivability of a patient. The method includes storing in an electronic database patient health data comprising a plurality of sets of data, each set having a first parameter relating to heart rate variability data including at least one of ST segment elevation and depression, a second parameter relating to vital sign data, and a third parameter relating to patient survivability; providing a network of nodes interconnected to form an artificial neural network, the nodes comprising a plurality of neurons, each having at least one input with an associated weight; and training the neural network using the patient health data such that the associated weight of the at least one input of each neuron is adjusted in response to respective first, second and third parameters of different sets of data from the patient health data, such that the neural network is trained to produce a prediction on the survivability of a patient within the next 72 hours.Type: GrantFiled: December 12, 2014Date of Patent: March 29, 2016Assignees: SINGAPORE HEALTH SERVICES PTE LTD., NANYANG TECHNOLOGICAL UNIVERSITYInventors: Marcus Eng Hock Ong, Zhiping Lin, Wee Ser, Guangbin Huang
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Publication number: 20160022162Abstract: The present disclosure provides a system and method of determining a risk score for triage. In particular, a system is provided for providing an assessment of risk of a cardiac event for a patient, for example an incoming patient to a hospital emergency department complaining of chest pain. In the disclosure, the system includes an input device for measuring physiological data based vital signs parameter of the patient, a twelve-lead electrocardiogram (ECG) device for establishing an ECG obtained from results of the electrocardiography procedure, and determining an ECG parameter and a heart rate variability (HRV) parameter therefrom. An ensemble-based scoring system is further provided, establishing weighted classifier based on past patient data and where the vital signs parameter, the ECG parameter and the HRV parameter are compared to corresponding weighted classifiers to determine a risk score. A corresponding method to determine a risk score for triage is also provided.Type: ApplicationFiled: March 7, 2014Publication date: January 28, 2016Inventors: Marcus Eng Hock Ong, Nan Liu
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Publication number: 20150223759Abstract: A method of predicting survivability of a patient. The method includes storing in an electronic database patient health data comprising a plurality of sets of data, each set having a first parameter relating to heart rate variability data including at least one of ST segment elevation and depression, a second parameter relating to vital sign data, and a third parameter relating to patient survivability; providing a network of nodes interconnected to form an artificial neural network, the nodes comprising a plurality of neurons, each having at least one input with an associated weight; and training the neural network using the patient health data such that the associated weight of the at least one input of each neuron is adjusted in response to respective first, second and third parameters of different sets of data from the patient health data, such that the neural network is trained to produce a prediction on the survivability of a patient within the next 72 hours.Type: ApplicationFiled: December 12, 2014Publication date: August 13, 2015Inventors: Marcus Eng Hock Ong, Zhiping Lin, Wee Ser, Guangbin Huang
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Publication number: 20150150468Abstract: A method of producing an artificial neural network capable of predicting the survivability of a patient, including: storing in an electronic database patient health data comprising a plurality of sets of data, each set having at least one of a first parameter relating to heart rate variability data and a second parameter relating to vital sign data, each set further having a third parameter relating to patient survivability; providing a network of nodes interconnected to form an artificial neural network, the nodes comprising a plurality of artificial neurons, each artificial neuron having at least one input with an associated weight; and training the artificial neural network using the patient health data such that the associated weight of the at least one input of each artificial neuron is adjusted in response to respective first, second and third parameters of different sets of data from the patient health data.Type: ApplicationFiled: February 6, 2015Publication date: June 4, 2015Inventors: Marcus Eng Hock Ong, Zhiping Lin, Wee Ser, Guangbin Huang
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Patent number: 8951193Abstract: A method of producing an artificial neural network capable of predicting the survivability of a patient, including: storing in an electronic database patient health data comprising a plurality of sets of data, each set having at least one of a first parameter relating to heart rate variability data and a second parameter relating to vital sign data, each set further having a third parameter relating to patient survivability; providing a network of nodes interconnected to form an artificial neural network, the nodes comprising a plurality of artificial neurons, each artificial neuron having at least one input with an associated weight; and training the artificial neural network using the patient health data such that the associated weight of the at least one input of each artificial neuron is adjusted in response to respective first, second and third parameters of different sets of data from the patient health data.Type: GrantFiled: March 6, 2014Date of Patent: February 10, 2015Assignees: Singapore Health Services Pte Ltd., Nanyang Technological UniversityInventors: Marcus Eng Hock Ong, Zhiping Lin, Wee Ser, Guangbin Huang
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Patent number: 8932220Abstract: A method of producing an artificial neural network capable of predicting the survivability of a patient, including: storing in an electronic database patient health data comprising a plurality of sets of data, each set having at least one of a first parameter relating to heart rate variability data and a second parameter relating to vital sign data, each set further having a third parameter relating to patient survivability; providing a network of nodes interconnected to form an artificial neural network, the nodes comprising a plurality of artificial neurons, each artificial neuron having at least one input with an associated weight; and training the artificial neural network using the patient health data such that the associated weight of the at least one input of each artificial neuron is adjusted in response to respective first, second and third parameters of different sets of data from the patient health data.Type: GrantFiled: March 6, 2014Date of Patent: January 13, 2015Assignees: Singapore Health Services Pte Ltd., Nanyang Technological UniversityInventors: Marcus Eng Hock Ong, Zhiping Lin, Wee Ser, Guangbin Huang
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Publication number: 20140257063Abstract: A method of producing an artificial neural network capable of predicting the survivability of a patient, including: storing in an electronic database patient health data comprising a plurality of sets of data, each set having at least one of a first parameter relating to heart rate variability data and a second parameter relating to vital sign data, each set further having a third parameter relating to patient survivability; providing a network of nodes interconnected to form an artificial neural network, the nodes comprising a plurality of artificial neurons, each artificial neuron having at least one input with an associated weight; and training the artificial neural network using the patient health data such that the associated weight of the at least one input of each artificial neuron is adjusted in response to respective first, second and third parameters of different sets of data from the patient health data.Type: ApplicationFiled: March 6, 2014Publication date: September 11, 2014Applicants: NANYANG TECHNOLOGICAL UNIVERSITY, SINGAPORE HEALTH SERVICES PTE LTD.Inventors: Marcus Eng Hock Ong, Zhiping Lin, Wee Ser, Guangbin Huang
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Publication number: 20140187988Abstract: A method of producing an artificial neural network capable of predicting the survivability of a patient, including: storing in an electronic database patient health data comprising a plurality of sets of data, each set having at least one of a first parameter relating to heart rate variability data and a second parameter relating to vital sign data, each set further having a third parameter relating to patient survivability; providing a network of nodes interconnected to form an artificial neural network, the nodes comprising a plurality of artificial neurons, each artificial neuron having at least one input with an associated weight; and training the artificial neural network using the patient health data such that the associated weight of the at least one input of each artificial neuron is adjusted in response to respective first, second and third parameters of different sets of data from the patient health data.Type: ApplicationFiled: March 6, 2014Publication date: July 3, 2014Applicants: NANYANG TECHNOLOGICAL UNIVERSITY, SINGAPORE HEALTH SERVICES PTE LTD.Inventors: Marcus Eng Hock Ong, Zhiping Lin, Wee Ser, Guangbin Huang
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Patent number: 8668644Abstract: A method of producing an artificial neural network capable of predicting the survivability of a patient, including: storing in an electronic database patient health data comprising a plurality of sets of data, each set having at least one of a first parameter relating to heart rate variability data and a second parameter relating to vital sign data, each set further having a third parameter relating to patient survivability; providing a network of nodes interconnected to form an artificial neural network, the nodes comprising a plurality of artificial neurons, each artificial neuron having at least one input with an associated weight; and training the artificial neural network using the patient health data such that the associated weight of the at least one input of each artificial neuron is adjusted in response to respective first, second and third parameters of different sets of data from the patient health data.Type: GrantFiled: April 23, 2013Date of Patent: March 11, 2014Assignees: Singapore Health Services Pte Ltd., Nanyang Technological UniversityInventors: Marcus Eng Hock Ong, Zhiping Lin, Wee Ser, Guangbin Huang